Semiparametric Density Forecasts of Daily Financial Returns from Intraday Data

In this article we propose a new method for producing semiparametric density forecasts for daily financial returns from high-frequency intraday data. The daily return density is estimated directly from intraday observations that have been appropriately rescaled using results from the theory of unifractal processes. The method preserves information concerning both the magnitude and sign of the intraday returns and allows them to influence all properties of the daily return density via the use of nonparametric specifications for the daily return distribution. The out-of-sample density forecasting performance of the method is shown to be competitive with existing methods based on intraday data for exchange rate and equity index data.